There is a recognized need for data access systems and analytical tools that provide semantic interoperability among different information sources in a given application domain, both within a particular enterprise and across enterprise boundaries. Some tools that have been developed for this purpose make use of ontologies. An ontology is a structured vocabulary that represents the schematic metadata of a particular application domain. The ontology provides a unified, semantic model of the information in the domain, including both the types of entities that the information may include and relationships among the entities. The ontology allows users to express query concepts and relationships in high-level terms, which are then translated by appropriate agents into lower-level database schemata and semantic analyses.
One of the early tools of this sort was InfoSleuth™, developed at MCC (Microelectronics and Computer Technology Corporation, Austin, Tex.). InfoSleuth is described, for example, in an article by Fowler et al., entitled “Agent-Based Semantic Interoperability in InfoSleuth,” SIGMOD Record 28:1 (March, 1999), pp. 60-67, which is incorporated herein by reference. InfoSleuth is an agent-based system, in which a set, or community, of agents collaborate at a semantic level to execute information gathering and analysis tasks. The underlying information sources can be diverse in both structure and content. The agents, which are coded in Java™, communicate at the semantic level over ontologies using a Knowledge Query Manipulation Language (KQML). Agent types defined by InfoSleuth include:                User agents, which provide a system interface that enables the user to communicate with the system.        Broker agents, which match requests for services or information with agents that can provide them.        An ontology agent, which serves the set of ontologies supported by InfoSleuth and provides details of the ontology on demand,        Resource agents, which translate queries and data between the local forms in which they are stored and their InfoSleuth forms.        Value mapping agents, which convert queries and results between common acceptable forms and a canonical form defined by the ontology.        Multi-resource query agents, which handle the decomposition and distribution of sub-queries to various resource agents and then recompose the results.        
Agents communicate and determine each other's capabilities using a shared ontological model of information management. The ontology provides the semantic framework for information activities in the domain of interest to the user. Semantic brokering allows agents to advertise their capabilities and to identify potential collaborators based on their advertised capabilities. The user may access the resources of the agent community from any location, and need know nothing about the physical location or structural characteristics of any resource.
Another method for distributed query handling is described by Wynblatt et al., in U.S. Patent Application Publication US 2002/0143755, whose disclosure is incorporated herein by reference. According to this method, a traditional database query is converted into network messages, which are routed to those data sources that have relevant data. The messages may be routed either directly or through designated query nodes. The data sources then send reply messages either directly to the originator of the query or via designated join nodes. In some embodiments, the data sources may be able to perform local join operations. The system collects the reply messages, and the messages that meet the requirements of the query are sent back to the query originator for presentation as a traditional database result.
Unicorn Solutions Inc. (New York, N.Y.) offers a Semantic Information Management (SIM) System, which it describes as a comprehensive platform for managing and integrating enterprise information resources. The system combines metadata repository, information modeling, hub-and-spoke mapping, and automated data transformation script generation capabilities. It is said to provide customers with a seamless business view by relating disparate data formats and interfaces to an information model that describes the business, its component parts, and all relationships. The system is described further in an article by Schreiber, entitled “Semantic Information Management (SIM): Solving the Enterprise Data Problem by Managing Data Based on its Business Meaning” (2003), which is available at www.unicorn.com and is incorporated herein by reference.
Various aspects of the Unicorn system are described in the patent literature. For example, U.S. Patent Application Publication US 2003/0163597, to Hellman et al., describes a method and system for collaborative ontology modeling, for use in building up an ontology from individual ontology efforts distributed over the Web. U.S. Patent Application Publication US 2004/0093344, to Berger et al., describes a method for mapping data schemas into an ontology model. U.S. Patent Application Publication US 2003/0101170, to Edelstein et al., describes a data query system using a central ontology model, in which a query processor generates a query in a data schema query language corresponding to a specified query expressed in an ontology query language. U.S. Patent Application Publication US 2003/0163450, to Borenstein et al., describes a method for providing a semantic registry for Web services and other services, based on an ontology model. The method is said to enable dynamic Web service integration by overcoming problems of semantic inconsistency. The disclosures of all the above patent application publications are incorporated herein by reference.